{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a7da67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e096b618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100.    4.    9.3]\n",
      " [ 50.    3.    4.8]\n",
      " [100.    4.    8.9]\n",
      " [100.    2.    6.5]\n",
      " [ 50.    2.    4.2]\n",
      " [ 80.    2.    6.2]\n",
      " [ 75.    3.    7.4]\n",
      " [ 65.    4.    6. ]\n",
      " [ 90.    3.    7.6]\n",
      " [ 90.    2.    6.1]]\n"
     ]
    }
   ],
   "source": [
    "# 读入数据\n",
    "data = genfromtxt(r\"Delivery.csv\",delimiter=',')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd7bd99d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100.   4.]\n",
      " [ 50.   3.]\n",
      " [100.   4.]\n",
      " [100.   2.]\n",
      " [ 50.   2.]\n",
      " [ 80.   2.]\n",
      " [ 75.   3.]\n",
      " [ 65.   4.]\n",
      " [ 90.   3.]\n",
      " [ 90.   2.]]\n",
      "[9.3 4.8 8.9 6.5 4.2 6.2 7.4 6.  7.6 6.1]\n"
     ]
    }
   ],
   "source": [
    "# 切分数据\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "print(x_data)\n",
    "print(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f235d763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建模型\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb1015f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients: [0.0611346  0.92342537]\n",
      "intercept: -0.868701466781709\n",
      "predict: [9.06072908]\n"
     ]
    }
   ],
   "source": [
    "# 系数\n",
    "print(\"coefficients:\",model.coef_)\n",
    "# 截距\n",
    "print(\"intercept:\",model.intercept_)\n",
    "\n",
    "# 测试\n",
    "x_test = [[102,4]]\n",
    "predict = model.predict(x_test)\n",
    "print(\"predict:\",predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "366c13a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().add_subplot(111,projection='3d')\n",
    "ax.scatter(x_data[:,0],x_data[:,1],y_data,c='r',marker = 'o',s = 100)\n",
    "x0 = x_data[:,0]\n",
    "x1 = x_data[:,1]\n",
    "# 生成网格矩阵\n",
    "x0,x1 = np.meshgrid(x0,x1)\n",
    "z = model.intercept_+x0*model.coef_[0]+x1*model.coef_[1]\n",
    "# 画3D图\n",
    "ax.plot_surface(x0,x1,z)\n",
    "# 设置坐标轴\n",
    "ax.set_xlabel('Miles')\n",
    "ax.set_ylabel('Num of Deliveries')\n",
    "ax.set_zlabel('Time')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
